# LangGraph - Detailed Guide

> This file contains the detailed procedure and reference material extracted from `SKILL.md` for focused loading. The root skill defines activation, examples, safety constraints, and limitations.

## Capabilities

- Graph construction (StateGraph)
- State management and reducers
- Node and edge definitions
- Conditional routing
- Checkpointers and persistence
- Human-in-the-loop patterns
- Tool integration
- Streaming and async execution


## Scope

- 0: Examples here are Python; verify language/package version compatibility
- 1: Learning curve for graph concepts
- 2: State management complexity
- 3: Debugging can be challenging


## Ecosystem

### Primary

- LangGraph
- LangChain
- LangSmith (observability)

### Common_integrations

- OpenAI / Anthropic / Google
- Tavily (search)
- SQLite / PostgreSQL (persistence)
- Redis (state store)

### Platforms

- Python applications
- FastAPI / Flask backends
- Cloud deployments


Examples are fragments sharing imports and graph definitions; the search tool and `safe_math_evaluator` must be supplied and bounded by the application. Never use unrestricted eval. Checkpointer packages are separate installs; isolate authenticated tenant/thread access and redact retained state. A graph state flag is not independent user authorization.

## Patterns

### Basic Agent Graph

Simple ReAct-style agent with tools

**When to use**: Single agent with tool calling

from typing import Annotated, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

# 1. Define State
class AgentState(TypedDict):
    messages: Annotated[list, add_messages]
    # add_messages appends new IDs and replaces messages sharing an existing ID

# 2. Define Tools
@tool
def search(query: str) -> str:
    """Search the web for information."""
    # Implementation here
    return f"Results for: {query}"

@tool
def calculator(expression: str) -> str:
    """Evaluate a math expression."""
    return str(safe_math_evaluator(expression))

tools = [search, calculator]

# 3. Create LLM with tools
llm = ChatOpenAI(model="gpt-4o").bind_tools(tools)

# 4. Define Nodes
def agent(state: AgentState) -> dict:
    """The agent node - calls LLM."""
    response = llm.invoke(state["messages"])
    return {"messages": [response]}

# Tool node handles tool execution
tool_node = ToolNode(tools)

# 5. Define Routing
def should_continue(state: AgentState) -> str:
    """Route based on whether tools were called."""
    last_message = state["messages"][-1]
    if last_message.tool_calls:
        return "tools"
    return END

# 6. Build Graph
graph = StateGraph(AgentState)

# Add nodes
graph.add_node("agent", agent)
graph.add_node("tools", tool_node)

# Add edges
graph.add_edge(START, "agent")
graph.add_conditional_edges("agent", should_continue, ["tools", END])
graph.add_edge("tools", "agent")  # Loop back

# Compile
app = graph.compile()

# 7. Run
result = app.invoke({
    "messages": [("user", "What is 25 * 4?")]
}, config={"recursion_limit": 20})

### State with Reducers

Complex state management with custom reducers

**When to use**: Multiple agents updating shared state

from typing import Annotated, TypedDict
from operator import add
from langgraph.graph import StateGraph

# Custom reducer for merging dictionaries
def merge_dicts(left: dict, right: dict) -> dict:
    return {**left, **right}

# State with multiple reducers
class ResearchState(TypedDict):
    # New IDs append; matching IDs replace
    messages: Annotated[list, add_messages]

    # Research findings merge
    findings: Annotated[dict, merge_dicts]

    # Sources accumulate
    sources: Annotated[list[str], add]

    # Current step (overwrites - no reducer)
    current_step: str

    # Error count (custom reducer)
    errors: Annotated[int, lambda a, b: a + b]

# Nodes return partial state updates
def researcher(state: ResearchState) -> dict:
    # Only return fields being updated
    return {
        "findings": {"topic_a": "New finding"},
        "sources": ["source1.com"],
        "current_step": "researching"
    }

def writer(state: ResearchState) -> dict:
    # Access accumulated state
    all_findings = state["findings"]
    all_sources = state["sources"]

    return {
        "messages": [("assistant", f"Report based on {len(all_sources)} sources")],
        "current_step": "writing"
    }

# Build graph
graph = StateGraph(ResearchState)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
# ... add edges

### Conditional Branching

Route to different paths based on state

**When to use**: Multiple possible workflows

from langgraph.graph import StateGraph, START, END

class RouterState(TypedDict):
    query: str
    query_type: str
    result: str

def classifier(state: RouterState) -> dict:
    """Classify the query type."""
    query = state["query"].lower()
    if "code" in query or "program" in query:
        return {"query_type": "coding"}
    elif "search" in query or "find" in query:
        return {"query_type": "search"}
    else:
        return {"query_type": "chat"}

def coding_agent(state: RouterState) -> dict:
    return {"result": "Here's your code..."}

def search_agent(state: RouterState) -> dict:
    return {"result": "Search results..."}

def chat_agent(state: RouterState) -> dict:
    return {"result": "Let me help..."}

# Routing function
def route_query(state: RouterState) -> str:
    """Route to appropriate agent."""
    query_type = state["query_type"]
    return query_type  # Returns node name

# Build graph
graph = StateGraph(RouterState)

graph.add_node("classifier", classifier)
graph.add_node("coding", coding_agent)
graph.add_node("search", search_agent)
graph.add_node("chat", chat_agent)

graph.add_edge(START, "classifier")

# Conditional edges from classifier
graph.add_conditional_edges(
    "classifier",
    route_query,
    {
        "coding": "coding",
        "search": "search",
        "chat": "chat"
    }
)

# All agents lead to END
graph.add_edge("coding", END)
graph.add_edge("search", END)
graph.add_edge("chat", END)

app = graph.compile()

### Persistence with Checkpointer

Save and resume agent state

**When to use**: Multi-turn conversations, long-running agents

from langgraph.graph import StateGraph
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.checkpoint.postgres import PostgresSaver

# SQLite for development
from contextlib import ExitStack
resources = ExitStack()
# Keep resources open for every graph invocation; close it on application shutdown.
memory = resources.enter_context(SqliteSaver.from_conn_string("agent_state.db"))

# PostgreSQL for production
# memory = resources.enter_context(PostgresSaver.from_conn_string(DATABASE_URL))
# memory.setup()  # initialize schema before use

# Compile with checkpointer
app = graph.compile(checkpointer=memory)

# Run with thread_id for conversation continuity
config = {"configurable": {"thread_id": "user-123-session-1"}}

# First message
result1 = app.invoke(
    {"messages": [("user", "My name is Alice")]},
    config=config
)

# Second message - agent remembers context
result2 = app.invoke(
    {"messages": [("user", "What's my name?")]},
    config=config
)
# Agent knows name is Alice!

# Get conversation history
state = app.get_state(config)
print(state.values["messages"])

# List all checkpoints
for checkpoint in app.get_state_history(config):
    print(checkpoint.config, checkpoint.values)

### Human-in-the-Loop

Pause for human approval before actions

**When to use**: Sensitive operations, review before execution

from langgraph.graph import StateGraph, START, END

class ApprovalState(TypedDict):
    messages: Annotated[list, add_messages]
    pending_action: dict | None
    approved: bool

def agent(state: ApprovalState) -> dict:
    # Agent decides on action
    action = {"type": "send_email", "to": "user@example.com"}
    return {
        "pending_action": action,
        "approved": False,
        "messages": [("assistant", f"I want to: {action}")]
    }

def execute_action(state: ApprovalState) -> dict:
    action = state["pending_action"]
    if state.get("approved") is not True:
        raise PermissionError("Action has not been approved")
    # Illustration only: replace with an idempotent authorized adapter.
    result = f"Simulated: {action['type']}"
    return {
        "messages": [("assistant", result)],
        "pending_action": None
    }

def should_execute(state: ApprovalState) -> str:
    return "execute"  # interrupt_before pauses before the action node

# Build graph
graph = StateGraph(ApprovalState)
graph.add_node("agent", agent)
graph.add_node("execute", execute_action)

graph.add_edge(START, "agent")
graph.add_conditional_edges("agent", should_execute, ["execute", END])
graph.add_edge("execute", END)

# Compile with interrupt_before for human review
app = graph.compile(
    checkpointer=memory,
    interrupt_before=["execute"]  # Pause before execution
)

# Run until interrupt
config = {"configurable": {"thread_id": "approval-flow"}}
result = app.invoke({"messages": [("user", "Send report")]}, config)

# Agent paused - get pending state
state = app.get_state(config)
pending = state.values["pending_action"]
print(f"Pending: {pending}")  # Human reviews

# Human approves - update state and continue
app.update_state(config, {"approved": True})
result = app.invoke(None, config)  # Resume

### Parallel Execution (Map-Reduce)

Run multiple branches in parallel

**When to use**: Parallel research, batch processing

from langgraph.graph import StateGraph, START, END
from langgraph.types import Send

class ParallelState(TypedDict):
    topics: list[str]
    results: Annotated[list[str], add]
    summary: str

def research_topic(state: dict) -> dict:
    """Research a single topic."""
    topic = state["topic"]
    result = f"Research on {topic}..."
    return {"results": [result]}

def summarize(state: ParallelState) -> dict:
    """Combine all research results."""
    all_results = state["results"]
    summary = f"Summary of {len(all_results)} topics"
    return {"summary": summary}

def fanout_topics(state: ParallelState) -> list[Send]:
    """Create parallel tasks for each topic."""
    return [
        Send("research", {"topic": topic})
        for topic in state["topics"]
    ]

# Build graph
graph = StateGraph(ParallelState)
graph.add_node("research", research_topic)
graph.add_node("summarize", summarize)

# Fan out to parallel research
graph.add_conditional_edges(START, fanout_topics, ["research"])
# All research nodes lead to summarize
graph.add_edge("research", "summarize")
graph.add_edge("summarize", END)

app = graph.compile()

result = app.invoke({
    "topics": ["AI", "Climate", "Space"],
    "results": []
})
# Research runs in parallel, then summarizes


## Collaboration

### Delegation Triggers

- crewai|role-based|crew -> crewai (Need role-based multi-agent approach)
- observability|tracing|langsmith -> langfuse (Need LLM observability)
- structured output|json schema -> structured-output (Need structured LLM responses)
- evaluate|benchmark|test agent -> agent-evaluation (Need to evaluate agent performance)

### Production Agent Stack

Skills: langgraph, langfuse, structured-output

Workflow:

```
1. Design agent graph with LangGraph
2. Add structured outputs for tool responses
3. Integrate Langfuse for observability
4. Test and monitor in production
```

### Multi-Agent System

Skills: langgraph, crewai, agent-communication

Workflow:

```
1. Design agent roles (CrewAI patterns)
2. Implement as LangGraph with subgraphs
3. Add inter-agent communication
4. Orchestrate with supervisor pattern
```

### Evaluated Agent

Skills: langgraph, agent-evaluation, langfuse

Workflow:

```
1. Build agent with LangGraph
2. Create evaluation suite
3. Monitor with Langfuse
4. Iterate based on metrics
```


## Related Skills

Works well with: `crewai`, `autonomous-agents`, `langfuse`, `structured-output`